IoT and Machine Learning-Based Personalized Human Accident Detection and Tracking System

Accidents pose a significant threat worldwide, often leading to severe harm and loss. Existing solutions mainly focus on vehicle-related accidents and rely heavily on smartphones, leaving a gap in detecting and alerting accidents outside vehicular contexts. This study proposes an IoT- and machine learning-based personalized accident detection and tracing system to address this limitation. The system comprises an IoT-enabled smart band equipped with sensors to monitor vital signs (heart rate, blood pressure, body temperature, and SpO2) and GPS for precise location tracking, a user-specific machine learning model to identify abnormal physiological states, and a cross-platform mobile application to deliver real-time emergency alerts and location information to responders. Sensor readings are transmitted via Wi-Fi to a cloud server, minimizing smartphone dependency and latency compared to GSM/GPRSbased systems. The ML model, trained on both public and locally collected datasets, achieved 99.44% accuracy using a Random Forest classifier.